(1) Field of the Invention
The present invention relates generally to television recommender systems, and more particularly, to collaborative sampling for implicit recommender systems.
(2) Description of Related Art
A television recommender system computes recommendation scores for upcoming television shows by comparing their features to features in a user profile that indicate likes and dislikes. A television recommender system learns the user profile from a user's viewing history. A machine-learning algorithm requires both positive and negative examples in order to provide a television recommendation that covers the positive examples and avoids the negative examples.
In a viewing history, the viewer usually provides only positive examples, i.e., shows that are actually watched. Therefore the recommendation system must somehow create negative examples. Methods for collecting negative examples are known in the art. One method involves taking negative examples at the same time as the positive examples are acquired. Another method is as follows: for each given show that is watched, a show is uniformly picked from the space of all shows in the previous seven days excluding shows that were watched. This forms the negative example base. Once a negative example is generated, any recommender could be used to learn the concept description of liked vs. disliked in order to predict what shows the user might wish to watch.
Another sampling technique is the adaptive sampling technique that chooses negative samples depending upon the specific attributes of the shows that have been watched by the user. Some examples of these attributes are time, station-call-sign, etc. More information regarding this technique can be found in co-pending U.S. patent application Ser. No. 09/819,286, the entire contents of which is incorporated herein by its reference.
Therefore it is an object of the present invention to provide methods and systems that overcome the disadvantages associated with the prior art.